Assessing the information content of ERP signals in schizophrenia using multivariate decoding methods

Abstract

Multivariate pattern classification (decoding) methods are commonly employed to study mechanisms of neurocognitive processing in typical individuals, where they can be used to quantify the information that is present in single-participant neural signals. These decoding methods are also potentially valuable in determining how the representation of information differs between psychiatric and non-psychiatric populations. Here, we examined ERPs from people with schizophrenia (PSZ) and healthy control subjects (HCS) in a working memory task that involved remembering 1, 3, or 5 items from one side of the display and ignoring the other side. We used the spatial pattern of ERPs to decode which side of the display was being held in working memory. One might expect that decoding accuracy would be inevitably lower in PSZ as a result of increased noise (i.e., greater trial-to-trial variability). However, we found that decoding accuracy was greater in PSZ than in HCS at memory load 1, consistent with previous research in which memory-related ERP signals were larger in PSZ than in HCS at memory load 1. We also observed that decoding accuracy was strongly related to the ratio of the memory-related ERP activity and the noise level. In addition, we found similar noise levels in PSZ and HCS, counter to the expectation that PSZ would exhibit greater trial-to-trial variability. Together, these results demonstrate that multivariate decoding methods can be validly applied at the individual-participant level to understand the nature of impaired cognitive function in a psychiatric population.

Keywords: ERP decoding; Schizophrenia; Working memory.

Fig. 1

Procedure and results from the study of Leonard et al. (2013). (A) Example of a single trial. Participants were instructed to remember the colors of the target shapes in the first display (either the circles or the rectangles) and report whether a color of target shape changed between the two displays. Shown here is a ‘no change’ trial at memory load 3 when the circles were the to-be-remembered targets. (B) Average CDA amplitude at each memory load for healthy control subjects (HCS) and people with schizophrenia (PSZ), averaged across the time period of interest (i.e., 400–1000 ms after the onset of the first array). Error bars indicate ± 1 S.E. * = p < .05, ** = p< .01. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

(A) Grand average ERP scalp maps (mean voltage from 400–1000 ms) of the remember-right minus remember-left difference in HCS and PSZ. In these difference maps, a negative value indicates greater negativity when attending to the left visual field, and a positive value indicates greater negativity when attending to the right visual field. (B) Decoding accuracy based on the average voltage from 400–1000 ms at each memory load for HCS (solid blue line) and PSZ (broken red line). Chance decoding would be 0.5. Error bars indicate ± 1 S.E. Blue and orange asterisks indicate significant above-chance decoding for a given group and memory load. The black asterisk at memory load one indicates a significant difference between HCS and PSZ. * = p < .05, * = p < .01, *** = p < .001. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Relationship between decoding accuracy and (A) RMS_Ratio, (B) RMS_Interaction, and (C) RMS_Noise at each memory load. Each dot represents a single participant. Average RMS_Ratio (D) RMS_Interaction (E) and RMS_Noise (F) for PSZ and HCS at each memory load. Error bars represent ± 1 S.E.